The support vector machine is a commonly used classification method for its good performance. The processes of most of the existing methods for multi-class problem are not simple. More than one support vector machine (SVM) classifier should be trained in each of these methods. In this paper, a novel multi-class support vector machine method is presented, named the Multi-class Least Square Suppo...

Support Vector Machines(SVMs) have been extensively researched in the data mining and machine learning communities for the last decade and actively applied to applications in various domains. SVMs are typically used for learning classification, regression, or ranking functions, for which they are called classifying SVM, support vector regression (SVR), or ranking SVM (or RankSVM) respectively. ...

The life expectancy and load capacity of oil-immersed power transformers are intimately associated with the winding hot spot temperature (HST). Thus, accurately predicting HSTs is essential in evaluating the life expectancy of power transformers and in preventing thermal failure. Previously, support vector machine (SVM) has been successfully employed to solve the regression problem of nonlinear...

In this paper, a new image denoising method based on wavelet analysis and support vector machine regression (SVR) is presented. The feasibility of image denoising via support vector regression is discussed and an illustrative example is given. The wavelet kernel is proposed to construct wavelet support vector machine (WSVM). The result of experiment shows that the denoising method based on WSVM...

It is well-known that accurate prediction for network flow is very important to meet the communication requirement of internet network. This study is to propose a novel twin support vector regression algorithm for network flow forecasting. The twin support vector regression algorithm is comprised of a pair of the standard SVR. In order to show the excellent performance of twin support vector re...

Loadability limits are critical points of particular interest in voltage stability assessment, indicating how much a system can be stressed from a given state before reaching instability. Thus estimating the loadability margin of a power system is essential in the real time voltage stability assessment. A new methodology is developed based on Support Vector Regression (SVR) which is the most co...

− Instead of minimizing the observed training error, Support Vector Regression (SVR) attempts to minimize the generalization error bound so as to achieve generalized performance. The idea of SVR is based on the computation of a linear regression function in a high dimensional feature space where the input data are mapped via a nonlinear function. SVR has been applied in various fields – time se...

This paper explores the possibilities of point cloud reduction using  insensitive support vector regression (-SVR).  -SVR is a technique that can carry out the regression using different kernel functions (sigmoid, radial basis function, B-spline, spline, etc.) and it is suitable for detection of flat regions and regions with high curvature in scanned data. Using  -SVR the density of preserv...

A number of machine learning (ML) techniques have recently been proposed to solve color constancy problem in computer vision. Neural networks (NNs) and support vector regression (SVR) in particular, have been shown to outperform many traditional color constancy algorithms. However, neither neural networks nor SVR were compared to simpler regression tools in those studies. In this article, we pr...